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Classification of images acquired with colposcopy using artificial neural networks
Priscyla W Simões1, Narjara B Izumi1, Ramon S Casagrande2
1Curso de Medicina, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil. ; Research Group of Tecnologia da Informação e Comunicação na Saúde, Universidade do Extremo Sul Catarinense (UNESC), Criciúma, Brazil.
Objective:
To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy.
Purpose:
Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database (n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used.
Results:
After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%.
Conclusion:
Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study.
